Introduces probability and statistical tools for data science, emphasizing simulation-based inference. Topics include descriptive statistics, probability distributions, parametric and nonparametric inference, and simulation techniques. Hands-on projects and R programming will develop practical skills for analyzing real-world data and solving complex analytical tasks.
Athena Title
Stat for Data Sci with R
Non-Traditional Format
This course will be taught 95% or more online.
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Student learning Outcomes
Students will compute measures of central tendency and dispersion, and create visualizations (histograms, boxplots, bar charts, scatter plots) to explore data distributions and relationships.
Students will grasp basic probability concepts, probability rules, and analyze discrete and continuous probability distributions (Bernoulli, Binomial, Normal).
Students will understand principles of statistical inference, the role of simulation techniques, and apply Monte Carlo methods for modeling and decision-making.
Students will use bootstrap, jackknife, and permutation tests to estimate confidence intervals, biases, and hypothesis testing.
Students will perform hypothesis tests (Z-tests, t-tests, F-tests, chi-square tests), apply rank-based nonparametric methods, and estimate parameters using Maximum Likelihood Estimation (MLE) and Method of Moments.
Students will implement kernel density estimation and nonparametric inference techniques.
Students will use cross-validation to evaluate and improve model performance.
Students will analyze real-world datasets using R, leveraging built-in functions and libraries for statistical analysis and visualization.
Students will design and execute hands-on projects and case studies to solve real-world data science problems, building a strong foundation for advanced statistical modeling.
Topical Outline
Descriptive Statistics:
• Measures of central tendency: mean, median, mode.
• Measures of dispersion: variance, standard deviation, range, quartiles.
• Visualization techniques: histograms, box plots, bar charts, scatter plots.
Descriptive Statistics:
• Measures of central tendency: mean, median, mode.
• Measures of dispersion: variance, standard deviation, range, quartiles.
• Visualization techniques: histograms, box plots, bar charts, scatter plots.
Introduction to Statistical Inference:
• Overview of statistical inference
• Parametric vs. nonparametric methods
• Importance of simulation in statistical inference
Simulation Techniques:
• Monte Carlo simulation
• Bootstrap resampling
• Permutation tests
